def plot_2E(ax: plt.Axes,
bin_edges_human: np.ndarray = np.array(
[-1000, -90, -60, -30, -4, -2, 0, 2, 4, 30, 60, 1000]),
bin_edges_model: np.ndarray = np.linspace(-160, 100, 40)):
Δ, p_human, p_model = [], [], []
for pid in DataExp1.pids:
data = DataExp1(pid)
model = data.build_model(models.BayesianIdealObserver)
df = model.predict(model.fit())
δ = np.stack([
np.log(df[s]) - np.log(np.sum(df.loc[:, df.columns != s], axis=1))
for s in data.structures
])
Δ += list(δ.T.flatten())
p_model += list(
data.cross_validate(models.ChoiceModel4Param).to_numpy().flatten())
p_human += list(
np.array([data.df['choice'] == s
for s in Exp1.structures]).T.flatten())
df = pd.DataFrame({'Δ': Δ, 'p_human': p_human, 'p_model': p_model})
x_human, y_human, yerr_human, x_model, y_model, yerr_model = [], [], [], [], [], []
df['bin'] = pd.cut(df['Δ'], bin_edges_human, labels=False)
for i in range(len(bin_edges_human) - 1):
_df = df[df['bin'] == i]
x_human.append(_df['Δ'].mean())
y_human.append(_df['p_human'].mean())
yerr_human.append(_df['p_human'].sem())
df['bin'] = pd.cut(df['Δ'], bin_edges_model, labels=False)
for i in range(len(bin_edges_model) - 1):
_df = df[df['bin'] == i]
x_model.append(_df['Δ'].mean())
y_model.append(_df['p_model'].mean())
yerr_model.append(_df['p_model'].sem())
ax.errorbar(x_human,
y_human,
yerr_human,
label='Human ± sem',
color=colors['decision_human'],
fmt='.',
capsize=2,
ms=2,
capthick=0.5,
zorder=1)
ax.plot(x_model,
y_model,
color=colors['decision_model'],
label='Model',
ms=1,
zorder=0)
ax.set_xlabel(r'logit( $P_\mathregular{ideal}$($S\,|\,\bf{X}$) )')
ax.set_ylabel(r'$P($choice=$S\,|\,\bf{X}$)')
ax.set_ylim(0, 1)
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles[::-1],
labels[::-1],
loc='upper left',
handler_map={ErrorbarContainer: HandlerErrorbar(yerr_size=0.25)})
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
plt.tight_layout()
def bayesian_confidence(ax: plt.Axes,
bin_edges: np.ndarray = np.array([-1000, -40, -20, -10, -4.5, -1.5, 1.5, 4.5, 10, 20, 40, 1000])):
x, y = [], []
for pid in DataExp1.pids:
data = DataExp1(pid)
model = data.build_model(models.ChoiceModel4Param)
L = model.L + model.L_uniform
x += list(logsumexp(L, b=model.is_chosen, axis=1) - logsumexp(L, b=1 - model.is_chosen, axis=1))
y += list((model.df['confidence'] == 'high').astype(float))
df = pd.DataFrame({'x': x, 'y': y})
df['bin'] = pd.cut(df['x'], bin_edges, labels=False)
x, y, yerr = [], [], []
for i in range(len(bin_edges) - 1):
_df = df[df['bin'] == i]
if len(_df) == 0:
continue
x.append(_df['x'].mean())
y.append(_df['y'].mean())
yerr.append(_df['y'].sem())
ax.errorbar(x, y, yerr, label='Human $\pm$ sem', c='darkgreen', fmt='.-', capsize=2, ms=2, capthick=0.5)
ax.set_yticks([0, 1])
ax.set_yticklabels(['Low', 'High'])
ax.set_ylabel('Avg. reported\nconfidence', labelpad=-16)
# ax.set_xticks([0, 0.5, 1])
ax.set_xlabel(r'logit$(\,P_{\mathregular{ideal}}(S\,|\,\bf{X}$$)\,)$')
ax.legend(loc='lower right', handler_map={ErrorbarContainer: HandlerErrorbar(yerr_size=0.25)})
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
plt.tight_layout()
def exp2all():
n_col = len(DataExp2.pids) // 3
_, axes = plt.subplots(3, n_col, figsize=(7.5, 5))
h, l = None, None
for i in range(len(DataExp2.pids)):
pid = DataExp2.pids[i]
ax = axes[i // n_col, i % n_col]
ax.set_title(f'#{i + 1}')
data = DataExp2(pid)
mpl.rcParams['font.size'] -= 2
h, l = data.plot_stacked_bar(ax, plot_legend=False)
mpl.rcParams['font.size'] += 2
y_human, err = data.plot_line_human()
ax.errorbar(DataExp2.x, y_human, err, label='Human $\pm$ sem', color=colors['decision_human'],
capsize=3, capthick=1, lw=1, ms=2, fmt='o', zorder=3)
m1 = data.load_model(models.ChoiceModel4Param, DataExp1(pid).build_model(models.ChoiceModel4Param).fit())
y1 = data.plot_line_model(m1.predict(m1.fit()))
ax.plot(DataExp2.x, y1, 'o--', label='Transfer model', color=colors['decision_transfer'], lw=1, ms=2, zorder=2)
y2 = data.plot_line_model(data.cross_validate(models.ChoiceModel4Param))
ax.plot(DataExp2.x, y2, 'o-', label='Fitted model', color=colors['decision_model'], lw=1, ms=2, zorder=2)
handles, labels = ax.get_legend_handles_labels()
h += handles[::-1]
l += labels[::-1]
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.set_xlabel(' ', labelpad=18)
plt.gcf().legend(h, l, loc='lower center', ncol=7,
handler_map={ErrorbarContainer: HandlerErrorbar(yerr_size=0.35)})
plt.tight_layout()
def make_errorbar_legend(ax: Axes = None) -> None:
"""size adjusted legend for error bars, default does not work well with different linestyles"""
if ax is None:
ax = pyplot.gca()
ax.legend(
handlelength=5,
handleheight=3,
handler_map={ErrorbarContainer: HandlerErrorbar(xerr_size=0.9, yerr_size=0.9)},
)
def plot_3B(ax: plt.Axes):
data = pool(DataExp2)
data.plot_stacked_bar(ax)
n = len(ExpConfig.glo_exp2)
y_human, y1, y2 = np.zeros(n), np.zeros(n), np.zeros(n)
for pid in DataExp2.pids:
data = DataExp2(pid)
y_human += data.plot_line_human()[0]
m1 = data.load_model(
models.ChoiceModel4Param,
DataExp1(pid).build_model(models.ChoiceModel4Param).fit())
y1 += data.plot_line_model(m1.predict(m1.fit()))
y2 += data.plot_line_model(
data.cross_validate(models.ChoiceModel4Param))
y_human, y1, y2 = y_human / len(DataExp2.pids), y1 / len(
DataExp2.pids), y2 / len(DataExp2.pids)
err = [np.sqrt(p * (1 - p) / len(DataExp2.pids) / 20) for p in y_human]
ax.errorbar(DataExp2.x,
y_human,
err,
label='Human $\pm$ sem',
color=colors['decision_human'],
capsize=5,
capthick=1,
lw=1,
ms=3,
fmt='o',
zorder=3)
ax.plot(DataExp2.x,
y1,
'o--',
label='Transfer model',
color=colors['decision_transfer'],
lw=1,
ms=3,
zorder=2)
ax.plot(DataExp2.x,
y2,
'o-',
label='Fitted model',
color=colors['decision_model'],
lw=1,
ms=3,
zorder=2)
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles[::-1],
labels[::-1],
loc='upper right',
handler_map={ErrorbarContainer: HandlerErrorbar(yerr_size=0.35)})
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
plt.tight_layout()
def create_artists(self, legend, orig_handle,
xdescent, ydescent, width, height, fontsize,
trans):
#from matplotlib.collections import LineCollection
# call the parent class function
artists = HandlerErrorbar.create_artists(self, legend, orig_handle,
xdescent, ydescent,
width, height, fontsize,
trans)
#Identify the artists. just so we know what each is
#NOTE: The order here is different to that in ErrorbarContainer, so we re-order
barlinecols, rest = partition(is_line, artists)
barlinecols = tuple(barlinecols)
*caplines, legline, legline_marker = rest
xerr_size, yerr_size = self.get_err_size(legend, xdescent, ydescent,
width, height, fontsize) #NOTE: second time calling this (already done in HandlerErrorbar.create_artists
legline_marker.set_pickradius( xerr_size*1.25 )
legline_marker.set_picker( ErrorbarPicker() )
return [legline_marker, caplines, barlinecols, legline]
def plot_3C(ax: plt.Axes,
bin_edges_human: np.ndarray = np.array(
[-1000, -4.5, -3, -1.5, 0, 1.5, 3, 4.5, 1000]),
bin_edges_model: np.ndarray = np.array(
[-1000, -4.5, -3, -1.5, 0, 1.5, 3, 4.5, 1000])):
Δ, p_human, p1, p2 = [], [], [], []
for pid in DataExp2.pids:
data = DataExp2(pid)
model = data.build_model(models.BayesianIdealObserver)
df = model.predict(model.fit())
Δ += list(np.log(df['C']) - np.log(df['H']))
model = data.load_model(
models.ChoiceModel4Param,
DataExp1(pid).build_model(models.ChoiceModel4Param).fit())
p1 += list(model.predict(model.fit())['C'])
p2 += list(data.cross_validate(models.ChoiceModel4Param)['C'])
p_human += list((data.df['choice'] == 'C') * 1.0)
df = pd.DataFrame({'Δ': Δ, 'p_human': p_human, 'p1': p1, 'p2': p2})
x_human, y_human, yerr_human, x_model, y1, yerr1, y2, yerr2 = [], [], [], [], [], [], [], []
df['bin'] = pd.cut(df['Δ'], bin_edges_human, labels=False)
for i in range(len(bin_edges_human) - 1):
_df = df[df['bin'] == i]
x_human.append(_df['Δ'].mean())
y_human.append(_df['p_human'].mean())
yerr_human.append(_df['p_human'].sem())
df['bin'] = pd.cut(df['Δ'], bin_edges_model, labels=False)
for i in range(len(bin_edges_model) - 1):
_df = df[df['bin'] == i]
x_model.append(_df['Δ'].mean())
y1.append(_df['p1'].mean())
yerr1.append(_df['p1'].sem())
y2.append(_df['p2'].mean())
yerr2.append(_df['p2'].sem())
ax.errorbar(x_human,
y_human,
yerr_human,
label='Human $\pm$ sem',
color=colors['decision_human'],
fmt='.',
capsize=2,
ms=2,
capthick=0.5,
zorder=1)
ax.plot(x_model,
y1,
'--',
color=colors['decision_transfer'],
label='Transfer model',
ms=1,
zorder=0)
ax.plot(x_model,
y2,
'-',
color=colors['decision_model'],
label='Fitted model',
ms=1,
zorder=0)
ax.set_xlabel(r'logit( $P_\mathregular{ideal}(S=C\,|\,\bf{X}$) )')
ax.set_ylabel(r'$P$(choice=$C\,|\,\bf{X}$)')
ax.set_ylim(0, 1)
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles[::-1],
labels[::-1],
loc='lower right',
handler_map={ErrorbarContainer: HandlerErrorbar(yerr_size=0.25)})
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
plt.tight_layout()
def __init__(self, xerr_size=0.5, yerr_size=None,
marker_pad=0.3, numpoints=None, **kw):
HandlerErrorbar.__init__(self, xerr_size, yerr_size,
marker_pad, numpoints, **kw)
self.eventson = True
# Plots
for var, histos in variables.iteritems():
histos = histos[0] # drop metadata
fig = Figure() # one figure per variable
canvas = FigureCanvasPdf(fig)
axes = fig.add_subplot(111) # row, col, id (121+j, when plotting both)
axes.grid(axis='y')
if title:
if bkgeff:
axes.set_title('Background rejection efficiency')
else:
axes.set_title('Signal selection efficiency')
axes.set_ylim(0, 3.5)
axes.set_ylabel('Efficiency (w/ offset)')
axes.set_xlim(*variables[var][1])
axes.set_xlabel(variables[var][2])
axes.xaxis.set_label_coords(0.9,-0.05)
for k, cut in enumerate(mva_cuts):
if not histos[k].GetEntries(): continue
x, y, yerr = th12errorbar(histos[k], yerr=True, asym=True)
axes.errorbar(x, y, yerr=yerr, xerr=None, fmt='none',
label='{}>{}'.format(classifier, cut))
axes.legend(fontsize=10, numpoints=1, frameon=False, ncol=ncuts,
handler_map={mpl.lines.Line2D: HandlerErrorbar()})
if doprint: pp.savefig(fig) # pp.savefig() for current fig
if doprint: pp.close()
def legend_darklight(axes, darklabel='Monte Carlo',
lightlabel='Analytic',
errtype=None, twosigma=False,
extralabel=None):
"""Makes a legend for the ax object 'axes' at the location
'loc', which indicates dark solid lines as 'MC' (or darklabel)
and light dotted lines as 'Analytic' (or lightlabel).
Parameters
----------
axes : axes.Axes
Description of parameter `axes`.
darklabel : str
Label of the dark objects in the legend.
lightlabel : str
Label of the light objects in the legend.
errtype : str
Specifies the plot type of the `dark` data,
and thus the corresponding legend icon.
None (default): legend for line plot.
yerr: legend for a plot with ecolor
modstyle: legend for a modstyle errorbar plot
twosigma : bool
Determines whether to include lighter, two sigma
error bars in the legend.
Returns
-------
type
Description of returned object.
"""
if errtype is None:
custom_lines = [Line2D([0], [0], **style_solid,
color=compcolors[(-1, 'dark')]),
Line2D([0], [0], **style_dashed,
color=compcolors[(-1, 'light')])]
axes.legend(custom_lines, [darklabel, lightlabel])
elif errtype == 'yerr':
_, xmax = axes.get_xlim()
axes.errorbar(xmax*50., 0, yerr=1., **style_yerr,
color='black', ecolor='gray', label=darklabel)
if twosigma:
axes.errorbar(xmax*50., 0, yerr=2., **style_yerr,
color='black', ecolor='lightgray',
label=darklabel)
if extralabel is not None:
axes.errorbar(xmax*50., 0, yerr=1., **style_yerr_ps,
color=compcolors[(-1, 'medium')],
ecolor=compcolors[(-1, 'light')],
label=extralabel)
if twosigma:
axes.errorbar(xmax*50., 0, yerr=2., **style_yerr_ps,
color=compcolors[(-1, 'medium')],
ecolor=compcolors[(-1, 'light')],
label=extralabel)
handles, _ = axes.get_legend_handles_labels()
if twosigma:
l = 0
# Setting up containers for both errorbars
if extralabel is not None:
twosig_extra = container.ErrorbarContainer(
handles[-1].lines, has_xerr=False,
has_yerr=True)
onesig_extra = container.ErrorbarContainer(
handles[-2].lines, has_xerr=False,
has_yerr=True)
l = -2
twosig = container.ErrorbarContainer(
handles[l-1].lines, has_xerr=False,
has_yerr=True)
# Setting up containers for both errorbars
onesig = container.ErrorbarContainer(
handles[l-2].lines, has_xerr=False,
has_yerr=True)
if extralabel is not None:
custom_handles = [(twosig, onesig),
(twosig_extra, onesig_extra)]
else:
custom_handles = [(twosig, onesig)]
else:
l = 0
# Setting up containers for both errorbars
if extralabel is not None:
onesig_extra = container.ErrorbarContainer(
handles[-1].lines, has_xerr=False,
has_yerr=True)
l=-1
onesig = container.ErrorbarContainer(
handles[l-1].lines, has_xerr=False,
has_yerr=True)
if extralabel is not None:
custom_handles = [onesig, onesig_extra]
else:
custom_handles = [onesig]
custom_handles.append(
Line2D([0], [0], **style_dashed, color=compcolors[(-1, 'light')])
)
if twosigma:
if extralabel is not None:
axes.legend(custom_handles, [darklabel, extralabel, lightlabel],
handler_map={
onesig: HandlerErrorbar(xerr_size=0.37),
twosig: HandlerErrorbar(xerr_size=0.65),
onesig_extra: HandlerErrorbar(xerr_size=0.37),
twosig_extra: HandlerErrorbar(xerr_size=0.65)}
)
else:
axes.legend(custom_handles, [darklabel, lightlabel],
handler_map={
onesig: HandlerErrorbar(xerr_size=0.37),
twosig: HandlerErrorbar(xerr_size=0.65)}
)
else:
if extralabel is not None:
axes.legend(custom_handles, [darklabel, extralabel, lightlabel])
else:
axes.legend(custom_handles, [darklabel, lightlabel])
elif errtype == 'modstyle':
_, xmax = axes.get_xlim()
axes.errorbar(xmax*50., 0, yerr=1., **modstyle,
color=compcolors[(-1, 'dark')],
label=darklabel)
if extralabel is not None:
axes.errorbar(xmax*50., 0, yerr=1., **modstyle_ps,
color=compcolors[(-1, 'medium')],
label=extralabel)
handles, _ = axes.get_legend_handles_labels()
# Setting up containers for errorbars
l = 0
if extralabel is not None:
onesig_extra = container.ErrorbarContainer(
handles[-1].lines, has_xerr=True,
has_yerr=True)
l=-1
onesig = container.ErrorbarContainer(handles[l-1].lines,
has_xerr=True,
has_yerr=True)
if extralabel is not None:
custom_handles = [onesig, onesig_extra]
else:
custom_handles = [onesig]
custom_handles.append(
Line2D([0], [0], **style_dashed, color=compcolors[(-1, 'light')])
)
if extralabel is not None:
axes.legend(custom_handles, [darklabel, extralabel, lightlabel],
prop={'size': 15}, loc='upper left')
else:
axes.legend(custom_handles, [darklabel, lightlabel])
# p9 = ax.plot([], [], color='tab:green', linewidth=0.5)
# p10 = ax.fill([], [], color='tab:green', alpha=0.45)
p0 = ax.errorbar([-3], [0], [2], fmt='o',
color='black', capsize=1.2,ms=2.5,elinewidth=0.6,mew=0.6)
# ax.legend()
def make_legend_polygon(legend, orig_handle,
xdescent, ydescent,
width, height, fontsize):
a=2*height
p = mpatches.Polygon(np.array([[0,-a/2],[width,-a/2],[width,height+a/2],[0,height+a/2],[0,-a/2]]))
return p
hm = {p0: HandlerErrorbar(xerr_size=0.9), p4[0]: HandlerPatch(patch_func=make_legend_polygon), p2[0]: HandlerPatch(patch_func=make_legend_polygon), p6[0]: HandlerPatch(patch_func=make_legend_polygon),p8[0]: HandlerPatch(patch_func=make_legend_polygon)}
l=ax.legend([(p3[0],p4[0]),(p5[0],p6[0]),(p7[0], p8[0]),(p1[0], p2[0]),p0], ['Gardner et al.'+'$^{5}$'+ ' (2003-2009):\ngravi., in-situ, elev. change','Wouters et al.'+'$^{19}$'+' (2002-2016):\ngravi.','Ciracì et al.'+'$^{20}$'+' (2002-2019):\ngravi.','Zemp et al.'+'$^{21}$'+' (2006-2015):\nin-situ, elev. change','This study\n(corresp. period):\nelev. change'],ncol=3,
handlelength=0.75,framealpha=0.8,loc='upper right',labelspacing=0.25,handler_map=hm,borderpad=0.4)
# ax.yaxis.grid(True,linestyle='--')
ax.tick_params(width=0.35,length=2.5)
l.get_frame().set_linewidth(0.5)
reg_dir = '/home/atom/ongoing/work_worldwide/vol/final'
list_fn_reg_multann = [os.path.join(reg_dir,'dh_'+str(i).zfill(2)+'_rgi60_int_base_reg_subperiods.csv') for i in np.arange(1,20)]
df_all = pd.DataFrame()
for fn_reg_multann in list_fn_reg_multann:
df_all= df_all.append(pd.read_csv(fn_reg_multann))
tlims = [np.datetime64('20'+str(i).zfill(2)+'-01-01') for i in range(21)]
list_df_glob = []
linestyle=':',
capsize=cap_size,
label='CLO w/ early prediction\n + Bayesian optimization')
# plt.xticks(np.arange(max_budget+1))
return h
h = plot_4c(ax5)
ax5.set_xlim((ax5.get_xlim()[0], ax5.get_xlim()[1]+1))
ax5.set_ylim((-1100, 23500))
xrange = ax5.get_xlim()[1] - ax5.get_xlim()[0]
yrange = ax5.get_ylim()[1] - ax5.get_ylim()[0]
ax5.set_aspect(aspect=xrange/yrange)
ax5.legend(frameon=False, loc='upper left', bbox_to_anchor=(-0.035, 1),
handler_map={type(h): HandlerErrorbar(xerr_size=0.6)})
ax5.set_ylabel('Experimental time (hours)')
ax5.set_xlabel('True cycle life of current best protocol\n(cycles)')
# Log inset
add_inset = False
if add_inset:
ax_ins = inset_axes(ax5, width='100%', height='100%', loc='upper left',
bbox_to_anchor=(0.12,0.28,0.5,0.38), bbox_transform=ax5.transAxes)
plot_4c(ax_ins)
ax_ins.set_xlim((825,900))
ax_ins.set_ylim((100,30000))
ax_ins.set_yscale('symlog')
# tight layout and save
plt.tight_layout()
